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Microsoft GH-600 Exam Syllabus Topics:

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?

A) A repository ruleset
B) A .copilotignore file
C) A CODEOWNERS file
D) Branch protection rules

2. Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration

You need to provide access to the API key of MCP1. The solution must meet the security requirements.
What should you do?

A) In product-api, add the API key as a GitHub Actions encrypted secret and reference the secret by using ${{ secrets.KEY }} in the workflow YAML of agent1.
B) Store the API key as a GitHub Codespaces user secret scoped to product-api.
C) Store the API key as a secret in the Copilot environment of product-api by using a name prefix of COPILOT_MCP_, and then reference the variable name in the mcp.json configuration.
D) In the product-api repository settings, add the API key directly to the .mcp/server.json file by using a plaintext apiKey field.

3. You have a GitHub repository that contains an agent named Orchestrator.Orchestrator delegates work to the following specialized subagents:
- Planner reviews issues and creates a plan of action.
- Implementer writes code based on the plan of action.
- Reviewer reviews the code.
You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
What should you do?

A) In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.
B) In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
C) In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
D) In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.

4. You have a GitHub Enterprise repository.
An agent opens pull requests to the main branch.
You need to ensure that changes to .github/workflows/* and /infra/* require approval from designated reviewers before merge.
What should you configure?

A) a branch protection rule and a CODEOWNERS file
B) a branch protection rule and copilot-instructions.md
C) a ruleset and an agents.md file
D) a ruleset and a .copilotignore file

5. You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) comments from Copilot code review that suggest improvements to code patterns
B) the detection of hardcoded secrets, such as API keys and tokens
C) the median time to merge metric reported on the Copilot usage metrics dashboard
D) linting errors from the repository's CI pipeline configured in GitHub Actions
E) CodeQL findings that identify security vulnerabilities in the generated code

Solutions: